DS004809#

Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories

Access recordings and metadata through EEGDash.

Citation: Haydn G. Herrema, Michael J. Kahana (2023). Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories. 10.18112/openneuro.ds004809.v2.2.0

Modality: ieeg Subjects: 258 Recordings: 7226 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004809

dataset = DS004809(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004809(cache_dir="./data", subject="01")

Advanced query

dataset = DS004809(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds004809,
  title = {Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories},
  author = {Haydn G. Herrema and Michael J. Kahana},
  doi = {10.18112/openneuro.ds004809.v2.2.0},
  url = {https://doi.org/10.18112/openneuro.ds004809.v2.2.0},
}

About This Dataset#

Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories

Description

This dataset contains behavioral events and intracranial electrophysiological recordings from a categorized free recall task. The experiment consists of participants studying a list of words, presented visually one at a time, completing simple arithmetic problems that function as a distractor, and then freely recalling the words from the just-presented list in any order. The data was collected at clinical sites across the country as part of a collaboration with the Computational Memory Lab at the University of Pennsylvania.

Unique to this paradigm is the semantic construction of the word lists. Each word comes from one of 25 semantic categories, and each list of 12 items contains 6 pairs of same-category words from 3 different categories. This means that each list has 4 words from 3 semantic categories, and in each half of the list there will be 1 pair of words from each category. For example, if a list contains words from categories A, B, and C, a possible list construction would be:

A1 - A2 - B1 - B2 - C1 - C2 - A3 - A4 - C3 - C4 - B3 - B4

To Note

  • The iEEG recordings are labeled either “monopolar” or “bipolar.” The monopolar recordings are referenced (typically a mastoid reference), but should always be re-referenced before analysis. The bipolar recordings are referenced according to a paired scheme indicated by the accompanying bipolar channels tables.

  • Each subject has a unique montage of electrode locations. MNI and Talairach coordinates are provided when available, along with brain region annotations.

  • Recordings were made on multiple different systems, so we have done the scaling to provide all voltage values in V.

Contact

For questions or inquiries, please contact sas-kahana-sysadmin@sas.upenn.edu.

Dataset Information#

Dataset ID

DS004809

Title

Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories

Year

2023

Authors

Haydn G. Herrema, Michael J. Kahana

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004809.v2.2.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004809,
  title = {Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories},
  author = {Haydn G. Herrema and Michael J. Kahana},
  doi = {10.18112/openneuro.ds004809.v2.2.0},
  url = {https://doi.org/10.18112/openneuro.ds004809.v2.2.0},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 258

  • Recordings: 7226

  • Tasks: 1

Channels & sampling rate

  • Channels: 126 (140), 124 (60), 108 (52), 125 (40), 139 (38), 128 (38), 88 (34), 120 (32), 127 (32), 116 (30), 148 (30), 145 (30), 131 (30), 112 (28), 196 (28), 64 (28), 110 (26), 179 (26), 142 (26), 118 (24), 155 (24), 121 (22), 114 (22), 133 (22), 90 (22), 159 (22), 251 (22), 92 (20), 94 (20), 186 (20), 113 (20), 178 (20), 115 (18), 158 (18), 198 (18), 152 (18), 105 (18), 104 (16), 156 (16), 247 (16), 183 (16), 200 (16), 68 (14), 98 (14), 122 (14), 166 (14), 106 (14), 212 (14), 76 (12), 100 (12), 109 (12), 241 (12), 240 (12), 150 (12), 184 (12), 78 (12), 154 (10), 250 (10), 168 (10), 165 (10), 208 (10), 56 (10), 72 (8), 97 (8), 180 (8), 192 (8), 164 (8), 189 (8), 141 (8), 224 (8), 188 (8), 134 (8), 175 (8), 219 (8), 173 (8), 238 (8), 185 (8), 89 (8), 70 (8), 167 (6), 160 (6), 83 (6), 207 (6), 229 (6), 60 (6), 46 (6), 162 (6), 130 (6), 95 (6), 220 (6), 209 (6), 140 (6), 151 (4), 177 (4), 84 (4), 161 (4), 203 (4), 169 (4), 119 (4), 123 (4), 187 (4), 193 (4), 176 (4), 67 (4), 132 (4), 96 (4), 53 (4), 93 (4), 172 (4), 63 (2), 85 (2), 102 (2), 182 (2), 75 (2), 239 (2), 86 (2), 16 (2), 52 (2), 136 (2), 14 (2), 80 (2), 146 (2), 218 (2), 202 (2), 26 (2), 143 (2), 153 (2), 107 (2), 36 (2), 243 (2), 163 (2), 37 (2), 62 (2), 99 (2), 111 (2), 213 (2), 50 (2), 206 (2)

  • Sampling rate (Hz): 1000.0 (1532), 500.0 (186), 1600.0 (20), 999.0 (16), 1023.999 (12), 1024.0 (8), 499.7071 (4)

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 477.2 GB

  • File count: 7226

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004809.v2.2.0

Provenance

API Reference#

Use the DS004809 class to access this dataset programmatically.

class eegdash.dataset.DS004809(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004809. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 252; recordings: 889; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004809 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004809

Examples

>>> from eegdash.dataset import DS004809
>>> dataset = DS004809(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#